Borrowing it
Nothing to install: this file belongs to tasqrai/tasqr-mcp-python. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tasqrai/tasqr-mcp-python/main/CLAUDE.mdgit clone --depth 1 https://github.com/tasqrai/tasqr-mcp-pythonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/tasqrai/tasqr-mcp-python/claude-md)<a href="https://agentmods.dev/instructions/tasqrai/tasqr-mcp-python/claude-md"><img src="https://agentmods.dev/badge/instructions/tasqrai/tasqr-mcp-python/claude-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.02893 | $0.02893 |
| Opus 5 | $0.01447 | $0.01447 |
| Sonnet 5 | $0.00579 | $0.00579 |
| Haiku 4.5 | $0.00289 | $0.00289 |
Grade A, and why
tasqr-mcp-python CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
What this is
tasqr-mcp is a thin local MCP proxy. It runs as a stdio process, authenticates with a Tasqr API key, and forwards every list_tools/call_tool request to the remote Tasqr Lambda MCP server over streamable HTTP (https://mcp.tasqr.ai/mcp by default). The actual task-management logic (task storage, claiming, tags, etc.) lives server-side — this repo does not implement it. The only substantial local logic is credential/auth bootstrapping and optional client-side (BYOK) encryption of task fields before they leave the process.
Commands
# Install for local dev (editable + dev deps)
pip install -e ".[dev]"
# Run the full test suite
uv run pytest # or: pytest
# Run a single test file / test
uv run pytest tests/test_crypto.py
uv run pytest tests/test_crypto.py::test_create_task_encrypts_title
# Lint / format
uv run ruff check .
uv run ruff format .
# Run against a local dev server instead of production
TASQR_MCP_URL=http://localhost:8000/mcp uvx tasqr-mcp
# Check version / run the CLI directly
tasqr-mcp --version
Lint and format with ruff check . and ruff format . (configured in pyproject.toml, line length 100). CI fails on either. There is no type checker configured.
Tests use pytest-anyio (pytest_plugins = ('anyio',) in tests/conftest.py), so any @pytest.mark.anyio test runs once under asyncio and once under trio — expect [asyncio]/[trio] variants in test output.
Architecture
Entry point (__main__.py): reads the API key via credentials.read_api_key(). If missing and stdin is a TTY, runs the GitHub Device Flow signup (device_flow.py) and persists the resulting key; otherwise exits with an error telling the user to run uvx tasqr-mcp interactively first. Then hands off to proxy.run(api_key).
Proxy loop (proxy.py): opens a streamable_http_client session to the upstream Tasqr MCP server, then starts a local mcp.server.Server over stdio that mirrors it — on_list_tools passes the upstream listing through directly (cursor included), on_call_tool optionally encrypts args before forwarding and decrypts the result after. Both handlers come from _handlers(upstream, crypto) and are passed to the Server constructor: the SDK registers handlers as constructor callables, not decorators, and building them apart from the server is what makes them testable (tests/test_handlers.py) without a live connection. The transport takes no headers= of its own — auth rides on an httpx2.AsyncClient the proxy builds and therefore closes (the transport only manages a client it created itself). A kms_key_id in the credentials config is necessary but not sufficient to encrypt: it only makes the proxy ask. The server has the final say (see BYOK below) and can refuse, in which case ClientCrypto is never constructed and the proxy exits with a ManagedOrgError. With no kms_key_id, calls pass straight through.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 82 lines · 2,893 tokens per session scan A bd5081435efd
tasqr-mcp-python CLAUDE.md is an instructions file published in the GitHub repository tasqrai/tasqr-mcp-python (0 stars, last pushed 7d ago), licensed MIT. It adds 2,893 tokens to every session, about $0.0145 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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